March 2nd, 2018
9:00 am to 5:00 pm
Reception and Poster Session
5:30 pm - 7:30 pm
Wick Alumni Center
University of Nebraska-Lincoln
1520 R St
The Holland Computing Center and the Quantitative Life Sciences Initiative have partnered to host the 2018 Supercomputing and Life Sciences (SLS) Symposium, bringing you speakers on computational sciences from across the nation and within the NU system.
Presentations will be evaluated and awarded prizes based on attendee votes. Graduate students are eligible to win our grand prize of a new Dell Workstation (valued at $1500)! For more information, check out the "Presenter Information" tab below.
|8:30 - 9:00||Check In and Coffee|
|9:00 - 9:10||Opening Remarks|
|9:10 - 9:50||The Past, Present, and Future of High Performance Computing in CADRE
BJ Lougee, HPC Engineer
Center for the Advancement of Data and Research in Economics (CADRE) - Federal Reserve Bank of Kansas City
|9:50 - 10:05||Break|
|10:05 - 10:45||Doug Jennewein, Director
Research Computing - University of South Dakota
|10:45 - 11:30||Dale Finkelson, Senior Program & Services Manager
|11:30 - 12:00||State of HCC
David Swanson, Director
Holland Computing Center - University of Nebraska
|12:00 - 12:45||Lunch|
|12:45 - 1:15||State of QLSI
Jennifer Clarke, Director
Quantitative Life Sciences Initative - University of Nebraska-Lincoln
|1:15 - 2:00||Unleashing your inner data scientist: Ability and Audacity to scale your science
Director - UA Data Science Institute
Co-PI - CyVerse
|2:00 - 2:20||Comparative genomics reveals earlier undetected plant growth promoting properties in anaerobic bacteria
Sanjay Antony Babu, Post-Doctoral Researcher
School of Biological Sciences - University of Nebraska-Lincoln
|2:20 - 3:05||What do patterns of gene flow among thousands of genomes reveal about bacterial species?
Erik Wright, Assistant Professor
Biomedical Informatics - University of Pittsburgh
|3:05 - 3:20||Break|
|3:20 - 4:05||Interaction between dietary peptides and gut microbiota- A road to opportunity
Rohita Sinha, Research Assistant Professor
Food Science and Technology - University of Nebraska-Lincoln
|4:05 - 4:50||Big Data for Precision Health: Interdisciplinary Informatics at UNO
Kate Cooper, Assistant Professor
School of Interdisciplinary Informatics - University of Nebraska Omaha
|4:50 - 5:30||Break|
|5:30 - 7:30||Evening Reception and Poster Session
Featuring the UNL Faculty Jazz Trio and cash bar.
Map and Directions:
The SLS Symposium will be held at the Wick Alumni Center on the University of Nebraska-Lincoln City campus.
Wick Alumni Center
1520 R St
Lincoln, NE 68508
Public parking is availble in the form of metered parking behind the Wick Alumni Center off of S street, at the corner of Q St. and Centennial Mall N, and at 14th and R St. Hourly garage parking is available at the 17th & R Street Garage or at the Larson Garage at 13th and Q.
The Holland Computing Center (HCC) and the Quantitative Life Sciences Initiative (QLSI) are hosting the Supercomputing and Life Sciences Symposium on March 2nd, 2018. We invite poster presentations covering research relevant to the QLSI mission and/or benefiting from advance computing (e.g. use of HCC, the Open Science Grid (OSG) or XSEDE resources). While this session is open to anyone with relevant research, current graduate students are strongly encouraged to participate.
Top presenters will be chosen based on the quality of research and their use of HCC services and/or application of Big Data in Life Sciences.
Prizes will be presented, based on attendee votes including the grand prize of a new Dell Workstation*. Additional prizes include gift cards and increased priority for HCC shared computing resources.
No need to create a new poster, presenters welcome to use existing posters.
Interested individuals should register and submit abstracts by February 28th to be considered.
*Eligibility for the Grand Prize is limited to Graduate Students only. Workstation Specs: Intel i7-6700, 16 GB DDR4 2133MHz RAM, 512 GB NVMe SSD, and AMD GPU FirePro
- Prizes will go preferentially to research that utilizes advanced computing resources such as HCC, XSEDE, OSG and/or features the application of Big Data in Life Science.
- Any topic related to advanced computing or big data, however, is welcome. A main goal is to spur and increase discussion and potential collaboration!
- Posters should be no more than 48" x 48" in size. For posters larger than this, please contact us at email@example.com to make arrangements.
The Past, Present, and Future of High Performance Computing in CADRE
HPC Engineer - Federal Reserve Bank of Kansas City
Federal Reserve Bank of Kansas City's (FRBKC) Center for the Advancement of Data and Research in Economics (CADRE) has transformed computing, and in particular High Performance Computing, for our researchers. With the added computational complexity of new economic models and the increase in the amount of data that our economic researchers use, CADRE needed to develop an environment where we could facilitate better research accommodating these new factors. By creating an environment that is flexible and allows almost any type of workload using almost any amount of data we have created a low-cost barrier of entry for our researchers to start utilizing HPC. We provide training and workshops around computational economics to provide the tools necessary for the advancement of economics in the HPC space. Thanks to this tailored environment, our researchers can more easily use our HPC resources for their work by utilizing larger datasets and completing complex economic models in a shorter amount of time leading to a faster turn around on research.
Unleashing your inner data scientist: Ability and Audacity to scale your science
Director - UA Data Science Institute
Co-PI - CyVerse
“Data deluge” has become a common phraseology for the phenomena across almost every scientific discipline. This talk will highlight some of the foundational tools and platforms that allow researchers to collaborate across disciplines and institutional boundaries, enabling them to effectively manage this deluge and share expertise.
Complementing these tools and platforms are emerging Machine Learning (ML) based algorithms and techniques that are opening new avenues for addressing data analysis and integration challenges generated by this deluge. Ability to effectively employ these Data Science based methods will require new forms of training, learning opportunities and technology orientation, this talk will discuss roadmap for augmenting expertise for domain scientists to acquire these new skills.
What do patterns of gene flow among thousands of genomes reveal about bacterial species?
Department of Biomedical Informatics, University of Pittsburgh
Biology is currently being revolutionized by dramatic decreases in the cost of DNA sequencing. This has enabled researchers to answer questions requiring large-scale analyses that can only be performed with high-throughput computing. To illustrate the power of this approach, I will describe how we used thousands of genomes to clarify the nature of bacterial species. These genomes exhibit a clear signature of vertical inheritance despite abundant horizontal gene transfers. This enables us to automatically delimit bacterial species in a manner that is consistent with historical convention. Furthermore, I will discuss the evolutionary implications of our findings and how this can be applied to develop new treatments for infectious disease.
Thermal Stress on Plants: Insights About Global Regulation and Metabolic Model Development
Mohammad Mazharul Islam, Jaspreet Sandhu, Harkamal Walia, Rajib Saha
Increased global temperature is detrimental to plant growth and development and significantly reduce crop yields. In rice, heat stress during early seed development adversely affects the seed size and quality at maturity. Plants use a suite of strategies to respond to abiotic stresses, including changing the abundance of stress-responsive genes/proteins that ultimately lead to the large-scale changes in gene expression levels, protein abundances, and metabolites. Complex gene-protein-reaction associations, as well as regulatory mechanisms, constitute a challenge to elucidate stress response mechanisms in plants. This research aims to understand the stress response mechanisms in developing rice seed using temporal transcriptomic analyses in control and stress conditions and using in silico metabolic analyses.
Rice plants were grown in a greenhouse under control conditions prior to flowering. A few days prior to flowering, plants were transferred to reach-in growth chambers with a 16h-light/8h-dark cycle at 28°C/ 25°C (control). Fertilized seeds were marked on two consecutive days and plants were maintained under control conditions. Plants were exposed to heat stress (35˚C) at 12 and 36 hours after fertilization (HAF) on day 1 and day 2, respectively, and young developing seeds were collected from control plants and stressed plants having two biological replicates. Total RNA isolated from developing seeds was used for differential gene expression analysis, which yielded ~7000 significantly stress-responsive genes. Clustering analysis based on Pearson’s correlation was used to develop minimal regulatory network and sub-networks, and identifying global regulators. The highly connected “hub” genes in the co-regulatory network included previously identified MADS-box genes as well as a large number of novel regulatory genes. A comprehensive, genome-scale metabolic model of rice seed is under development using draft genome reconstruction in ModelSEED and KBase databases and previous models. The coexpression information obtained from our experiments will be incorporated as regulatory constraints in the model.
Work on other tissue types of rice and modeling the interactions between them is underway. The important interactions of the rhizobiome with the plant root not only affects root metabolism, but significantly affects the plant metabolism in all tissue types. We are using multi-level and multi-objective modeling framework to integrate different tissue-specific models into a robust plant-scale model. This systems-level study will identify bottlenecks in the metabolic pathways and subsequently propose genetic intervention strategies improving crop yield. Our predictive mathematical model will come up with biologically important and non-intuitive solutions to problems related to stress response mechanisms, plant-microbiome interactions and developing tolerant plant species in an efficient and accurate fashion.
K-ATH - Towards a Multi-tissue Kinetic Model of Arabidopsis thaliana
Wheaton L. Schroeder and Rajib Saha
Computational modeling of metabolism is now an indispensable tool to drive the processes of understanding, discovering, and redesigning of biological systems. Although Flux Balance Analysis (FBA) is the primary tool used for this purpose, it has significant limitations due to the lack of reaction kinetics, chemical species concentration, and metabolic regulation. These are important to design engineering interventions directed to overproduction of a specific bioproduct or improvement of plant performance, particularly in the presence of feedback regulation. Arabidopsis thaliana has been a model system for modern plant science for the past three decades due to its small genome, short lifecycle, and ease of genetic manipulation. Several FBA models for A. thaliana already exist, and most regulatory interactions are well studied and documented. Hence, combining and advancing this knowledge in a kinetic model framework will allow us to answer important questions of regulation and tissue interaction in a model plant system.
Large-scale simulation studies enabled by HCC reveal the powers of GWAS approaches in dissecting highly polygenic traits in crop species
Chenyong Miao, Jinliang Yang, James C. Schnable
Improving the yield, quality and adaptability of crop species are the central goals of the modern agriculture. Genome-wide association study (GWAS) provides the ability to understand the genetic basis of complex traits and it has the greatest power to identify markers linked to genetic variants which are both common and have large effect sizes. Advent about 10 years ago, many traits associated genetic variants with large effect and commonly found across the populations have been identified. However, the strength and weakness of the GWAS panels in different crop species, especially through a comparative approach, have not been well explored. In this study, we obtained genotypic datasets for four crop species and simulated 8,000 phenotypic datasets with simulated genetic architectures ranging from simple, variation in the value of the trait across individuals controlled by a single gene, to complex, variation controlled by 1,024 genes. GWAS approaches such as general linear model (GLM), mixed linear model-based (MLM) methods, FarmCPU which is based on multi-locus mixed-model, as well as BayesCℼ approach were tested in the simulated datasets. The testing results show that the GLM based GWAS consistently showed the lowest power and accuracy across all scenarios and was omitted from downstream analyses. MLM-based GWAS methods have little power for detecting highly polygenic traits. The BayesC approach maintains its power and be able to detect trait-associated variants with small effects for highly polygenic traits. Furthermore, BayesCℼ provides additional advantages over other GWAS approaches in estimating the number of variants controlling for the phenotypic variation. The results present here will provide guidelines for further experimental design and results interpretation of GWAS.
Analyzing Dynamic Real World Graph using High Performance Computing
Network analysis has become an interesting tool for studying large-scale systems of interacting entities that arise in diverse domains such as bioinformatics, sociology, and epidemiology. Properties of networks (or graphs), such as centrality metrics, communities, can provide insights into the characteristics of the underlying systems. Since the networks are extremely large, parallel algorithms are essential for analyzing them. However, developing scalable parallel algorithms for networks is pretty challenging. This is because graph traversal is the primary component of many network algorithms. Traversal over unstructured data, such as networks, lead to irregular memory accesses resulting in low scalability and high computation costs. The problem is even more difficult when the networks are dynamic, that is, their structure changes with time. In my poster, I present a framework for creating fast and scalable parallel algorithms for updating properties of dynamic networks
BADPEP: A computational generated database of dietary peptides with putative antimicrobial and protein-protein interaction inhibitory activities
Qidong Jia, Jean-Jack M. Riethoven, Jennifer Clarke, and Rohita Sinha.
Background: The gut microbiome has emerged as a pseudo organ due to its enormous biochemical potential and taxonomic diversity. Compositional abnormalities (dysbiosis) in this microbiome have been associated with multiple metabolic disorders and diseases. Thus, a natural approach to treat metabolic disorders is the systematic manipulation of gut microbial composition; dietary molecules, the predominant factors affecting the gut microbiota, are an obvious route to achieve this. Dietary peptides are one such class of dietary molecules with antihypertensive, antioxidant and anti-inflammatory activities.
Result: Here we present the initial version of BADPEP (http://badpep.unl.edu/), a database of dietary peptides computationally screened for their antimicrobial activities and ability to competitively inhibit protein-protein interactions (PPIs). Inhibition of vital protein-protein interactions is a new drug development paradigm as few protein-protein interface fragments, also known as hot-segments, contribute most of the overall binding energy and can be used in the development of novel PPI inhibitors.
Method: The computational pipeline behind the BADPEP database mimics the human gastrointestinal digestion of dietary proteins by simulating the activities of the enzymes Pepsin, Trypsin and Chymotrypsin. The resulting set of in silico digested peptide fragments were compared with a known set of antimicrobial peptide (AMP) sequences and hot-segments of known protein-protein complexes. Dietary peptides with significant similarities with known AMPs and PPI interface-fragments can be easily queried through the BADPEP database. The Holland Computing Center’s clusters sandhill, tusker, and crane, together with the Open Science Grid are utilized in excess of 20.5 million CPU hours to support the computational aspects of this work. The underlying MySQL database itself is located on HCC’s cloud instance Anvil.
Conclusion: In the current framework, users can query the BADPEP database by submitting the Uniprot IDs of known protein sequences or they can query for all the bioactive-peptides present in a plant or animal species. We hope that BADPEP will assist in elucidating the mechanism of action of bioactive-peptides and contribute to the design of novel experiments to evaluate the bioactivities of dietary proteins.
Modeling Residual Stress Development in Hybrid Additive Manufacturing by Laser Peening
Guru Charan Reddy Madireddy; Michael P Sealy
Hybrid additive manufacturing (hybrid-AM) by laser peening is coupling additive manufacturing with surface treatments in a sequential manner to enhance mechanical properties of metals. Additive manufacturing of metals often results in parts with unfavorable mechanical properties. Laser shock peening is a high strain rate mechanical surface treatment that hammers a workpiece and induces favorable mechanical properties. Peening strain hardens a surface and imparts compressive residual stresses improving the mechanical properties of the material. The overarching objective of this work is to investigate the role LSP has on layer-by-layer processing of 3D printed metals. As the first study in this field, modeling hybrid-AM by LSP to understand the role of hybrid process parameters on temporal and spatial residual stress development. A finite element model was developed to help understand thermal and mechanical cancellation of residual stress when cyclically coupling printing and peening. Results indicate layer peening frequency is a critical process parameter and highly interdependent on the heat generated by the printing laser source. Optimum hybrid process conditions were found to exists that favorably enhance mechanical properties. With this demonstration, hybrid-AM has ushered in the next evolutionary step in additive manufacturing and has the potential to profoundly change the way high-value metal goods are manufactured.
First-Principles Studies of the Interactions Between Chemical Species inside Vanadium Redox Flow Batteries
Nadia N. Intan, Konstantin Klyukin, Vitaly Alexandrov
The operation of a large scale energy storage Redox Flow Battery (RFB)[i] is driven by reversible redox reactions of electro-active electrolyte species inside the two half-cells. The reversible redox reactions that happen during the charge â discharge cycles reuse all of the components of the system and thus eliminate all wastes. Therefore, RFBs should provide a solution to an environmentally clean energy alternative as all of the chemical wastes produced during the energy consumption process can be reversed by the charging process. One of the most studied system of RFBs is an all-Vanadium Redox Flow Battery (VRFB)[ii]. VRFB exploits the vast range of oxidation states that can be reached by vanadium. VRFB employs the redox couple of V2+/V3+ in the negative half-cell and VO2+/VO2+in the positive half-cell. The introduction of Naï¬on as the membrane in the system of VRFB leads to the formation of all sorts of complexes between vanadium of all oxidation states and Naï¬on. Vanadium â Naï¬on complexes block the pores on the Naï¬on, which reduces the performance of the battery, and leads to VRFB eventual degradation. Molecular level understanding of these interactions would provide insights into the chemistry of RFBs at the real operation temperature to aid the further improvement of RFBs.
Our computational studies[iii]Â from the DFT based gas phase calculations of the enthalpy of formation for the vanadium - Nafion complexes indicate that all four vanadium species have driving force to covalently bind to sulfonic acid group of Nafion ionomer unit via the formation of a single V-O bond. From the Car-Parrinello molecular dynamics based metadynamics simulations of the aqueous solution at 298 K we find that the formation of covalently bonded vanadium-Nafion complexes is spontaneous for V2+ and V3+, while V4+ and V5+ have a sizable activation barrier to attach to Nafion and diffuse away from SO3-group.
In addition, we analyze the calculated IR spectra for the most energetically favorable vanadium complexes with Nafion.
Atomistic Simulations on Twin Related Interactions in Magnesium
Mingyu Gong, Xinyan Xie, Jian Wang
Magnesium (Mg) and its alloys, as the lightest structural materials, have potential applications for vehicle and industry structures. However, Mg with the hexagonal closed pack (HCP) structure shows low strength and poor deformability due to the easy twinning. During cyclic loading, twins nucleate, grow and further interact with either dislocations or other twins. The interactions then affect further twinning and detwinning process. With Molecular Dynamics (MD) simulations, we studied the dislocation-twin and twin-twin interactions in Mg. The results show that twin could not only stop the gliding dislocations but also obstruct the propagation of the other twin variants. Both interactions contribute to the hardening effect of Mg.
Using Computational Methods to Study Fracture in Heterogeneous Material
Javad Mehrmashhadi, Yuye Tang, Quang Van Le, Florin Bobaru
Failure in heterogeneous material has been of interest to numerous researchers. In this research, failure performance of solder joint microstructure under drop test condition and dynamic crack growth in a weak/strong interface are studied using peridynamic theory. Peridynamics is an alternative theory of continuum mechanics which formulates problems in terms of integral equations rather than partial differential equations. In the past, apart from heterogeneous nature of the solder joint microstructure, the only fracture in homogenous material has been studied. However, the effect of the distribution of intermetallic compound (IMC) within the solder joint is significant. This paper investigates the impact of flash and thick Au coating on the failure of the solder joint under drop test conditions. Conditions include two different drop accelerations employed with the input acceleration (Input-G) method to calculate the model response. As a result, nodal velocities of nodes on the boundary of the solder are calculated and then used as input boundary conditions (BCs) for the peridynamic model (PD). The 2D bond-based PD model considers input BCs from the FE model. In our PD model, we introduce a volume-weighted approach to calculate mechanical properties of interface bonds. Also, we present a new criterion to break these bonds. The peridynamic results show significantly more fracture in samples with higher Au content compared to the flash samples, which correlates well with experiments. The peridynamic simulations capture the failure mechanism for solder joints with higher Au content: fracture through the AuSn4 intermetallic compound (IMC) and along the interface between the inclusions and the matrix.
Antibiotic Resistance Genes and Microbes in an Urban Stream during Wet Weather Conditions
Darshan Baral, Bruce Dvorak, David Admiraal, Xu Li
The impact of stormwater runoff on antibiotic resistance genes (ARGs) in urban watersheds is not well understood. ARGs and microbial taxa present in samples of water collected from Antelope Creek during wet weather conditions and in samples collected from environmental compartments representing immediate sources (base flow in the stream, runoff collected from storm drain outfalls, streambed sediment, embankment soil, and sanitary sewage) were characterized based on analysis of high throughput metagenomic sequencing data. Multidrug resistance genes were found to be the most common resistance genes across the environmental samples. Vancomycin resistance genes were found to be notable only in the embankment soil and street sweeping samples. Sanitary sewage was marked by a high abundance of resistance genes for tetracycline, beta-lactams, and Macrolide-Lincosamide-Streptogramin antibiotic types. Bayesian inference predicted storm drain outfall water to be the biggest contributor of microbes as well as ARGs in Antelope Creek during wet weather flows. Street sweepings were the biggest known contributor of both microbes and ARGs to storm drain outfall water. Several potential hosts of different ARGs were identified from network analysis of the strong correlations between abundance of ARGs and microbial genera.
Fracture and damage in porous materials
Sina Niazi, Ziguang Chen, and Florin Bobaru
We introduce a peridynamic (PD) model for simulating fracture and damage in porous materials based on an intermediate homogenization (IH) approach. In this approach, we do not represent the explicit geometry of the actual pores in the material, but neither do we fully homogenized the porous medium. The porosity is represented by initial peridynamic damage, introduced by stochastically pre-breaking mechanical bonds connecting to material nodes to achieve a desired porosity. We verify the model for elastodynamic problems using experimental data for wave propagation speed and apparent elastic modulus in a porous glass. The model is then used to study fracture behavior of Berea sandstone under three-point bending conditions and compared with the experimental observations as a validation test of our model in fracture problems. The experimental observations and the peridynamic simulations agree well in obtaining different crack paths depending on the length of the notch in sandstone samples with off-center notch under three-point bending conditions. A fully homogenized model for the porous beam fails to reproduce the sensitivity of crack evolution to the notch length. The results show the major effect local heterogeneities have on fracture behavior and the importance of the intermediate homogenization approach in modeling crack initiation and growth in porous materials.
Soil Microbial Diversity Across Maize Agroecosystems
Ashley Stengel, Rhae A. Drijber, Joshua R. Herr
While the presence of microorganisms on Earth is ubiquitous, there remain several gaps in our understanding of the diversity, and spatial and temporal variation within microbial environments, or microbiomes. This is particularly true for the soil, where it has been demonstrated that microbes influence plant health and growth, yet only a handful of these interactions have been well characterized. Bacteria, fungi, fauna, and plant roots all interact within the soil environment to shape soil food networks and drive ecosystem functioning. In the context of agricultural ecosystems, soil microbes may facilitate key ecological processes that foster healthy soils and resilient crops capable of meeting rising food demands. Given the importance of maize in meeting global food needs, our research explores the bacterial diversity of soils associated with maize croplands, one component of this complex ecosystem. To assess these complex and dynamic relationships high-performance computing (HPC) has become an essential tool. Drawing from publically available databases, we surveyed the bacterial 16S rRNA sequences of maize-associated soils from eight locations globally to ascertain the role of geography and management on bacterial community structure. Sequences were processed and analyzed using supercomputing resources available through the Holland Computing Center. Our results indicate that the bacterial communities in soils associated with maize are distinct from communities associated with other crops (e.g. soybean and alfalfa). Furthermore, we identify geographic location as the primary driver of variation in bacterial communities, with management practices such as tillage and rotation contributing secondarily to shifts in bacterial diversity. Characterizing this diversity is a crucial starting point for future efforts to determine how crop-associated soil microbial communities may facilitate resiliency and promote sustainability in agroecosystems.
Fast Computational Approaches For CT Image-based Segmentation and Reconstruction of Root Systems
Zheng Xu, Camilo Valdes, Stefan Gerth, Jennifer Clarke
Computed Tomography (CT) scanning technologies have been widely used in many scientific fields, especially in medicine and materials research. A lot of progress has been made in agronomic research thanks to CT technology. CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge.
We present new computational for faster and better segmentation and reconstruction of root systems from 3D CT scanning data. We propose new approaches within the category of voxel thresholding methods. Considering special characteristics of root systems, we propose our methods based on two new local-feature statistics, i.e., proportion and weighted proportion. We found that methods based on our two new statistics can calibrate root system magnitudes faster than traditional vessel-based approaches while preserve similar levels of performance. We illustrate and compare different approaches using both simulated and real CT scanning data.
Dynamic and Modularized MicroRNA Regulation and Its Implication in Human Cancers
Jiang Shu, Bruno Vieira Resende e Silva, Tian Gao, Zheng Xu, Juan Cui
High-throughput imaging of maize lines from public and private sectors employed in field trials
Zhikai Liang, Piyush Pandey, Vincent Stoerger, Yuhang Xu, Yumou Qiu, Yufeng Ge and James C. Schnable
You’re only as good as your Partner: Extremotolerant Fungi Survivability via Symbiosis
Carr E; Riekhof WR; Herr JR; Harris S
Biological soil crusts (BSCs) are a type of biofilm that can withstand a wide range of extreme conditions. Some types of conditions they can withstand are heat, freezing, desiccation, osmolarity, UV, heavy metals, and in some cases radiation. Some of the most common fungi in BSCs are the polyextremotolerant black yeasts in the Chaetothyriales, such as those in the genus Exophaila. Unique features of these organisms make them intriguing to study alone, but may also provide insight into the evolution of fungal-algal interactions. These polyextremotolerant fungi share similar resistance features with the lichens, therefore black yeasts may enable study of the lichen lifestyle without the challenges of directly working with lichens. In addition to sharing similar traits, black yeasts are also almost always found in the same location as algae, even in desert and polar ecosystems. We hypothesize that black yeasts and algae indeed interact in a mutualistic fashion that resembles lichens, and that observing these interactions will reveal clues about the nature of the fungal-algal communication that underlies the formation of BSCs. To test this hypothesis we have isolated black yeasts and algae from biological soil crusts and rock surfaces, identified isolates using their ITS sequences, subjected isolates to multiple stressors and nutritional conditions to investigate their phenotypic diversity. Culturing efforts and Sanger sequencing resulted in 24 black yeasts and 43 algal isolates from eight locations sampled within Jackman Flats, BC, Canada. In addition to these culturing efforts, we sequenced both ITS1 and 16S amplicons using Illumina sequencing to elucidate the microbial composition of the BSCs. Additionally, we have co-cultured isolated fungi and algae together in a pair-wise manner to observe phenotypes of their interactions. Current efforts are focused on integrating phylogenetic, phenotypic, and microbiome data to understand the taxonomy and function of BSCs.
Bioinformatics and Proteomics Evaluations to Consider IgE Binding Assays for Potential Cross-Reactivity Between House-Cricket (A
Mohamed Abdelmoteleb, Lee K. Palmer, Natasha Pavlovikj, Justin T. Marsh, Philip E. Johnson, and Richar
Humans have consumed insects throughout history. However regulators in the United States are asking for assurance that new food products containing processed, cultured insects are safe for crustacean allergic subjects, based on comparisons of genomic, transcriptomic or proteomic data. House-crickets (Acheta domesticus) are being used for food production and were the focus of this study of potential cross-reactivity. The transcriptome of the cricket was reported by the Malik lab (DOI: 10.755/elife.03676). We assembled their data using Velvet and Spades programs. Probable contiguous transcripts were identified using Blastx. Results were compared to the AllergenOnline.org database to find potentially significant alignments, focusing on allergens from the WHO/IUIS Allergen.org database: tropomyosins, arginine kinases, myosin light chain, troponin C, hemocyanin and triosephosphate isomerase. Predicted protein sequences were used to evaluate proteomic data of Aceta domesticus obtained by LC-MSMS to determine confirm the presence from a likely food preparation. Limited serum IgE studies were performed to identify shared IgE binding. Nucleotide sequences predicted protein sequences. The identities of cricket proteins were higher to cockroach than crustaceans’ proteins. The LC-MSMS confirmed the presence of a number of proteins. Serum IgE tests using a limited number of donors suggest differences in binding. Yet these data are preliminary and demonstrate the complexity answering regulatory questions. The abundance of the proteins and stability in food contribute to risks. Multiple IgE binding methods are needed to confirm cross-reactivity. This study shows that it is not yet possible to clearly determine risks for crustacean allergic subjects based only on sequence information.
Structural Evolution of Core-Shell Gold Nanoclusters: Aun − (n = 42−50)
Seema Pande, Wei Huang, Nan Shao, Lei-Ming Wang, Navneet Khetrapal, Wai-Ning Mei, Tian Jian, Lai-Sheng Wang, Xiao Cheng Zeng
The structures of large gold clusters are still not well known because of the challenges in global structural searches. Here we report a joint photoelectron spectroscopy (PES) and theoretical study of the structural evolution of negatively-charged core-shell gold nanoclusters. The combined PES data and density-functional calculations allow us to systematically identify the global minimum or candidates of the global minima of these relatively large gold nanoclusters, which are found to possess low-symmetry structures with gradually increasing core sizes. The present study sheds light in our understanding of the structural evolution as well as how the core-shell structures may start to embrace the golden pyramid (bulk-like) fragment.
Dynamic response of posterior eyeball subjected to blast loadings
Junfei Tong, Deepta Ghate, Sachin Kedar, Linxia Gu
Blast-induced traumatic optic neuropathy, i.e., acute injury to the optic nerve, gets increasing attention due to its prevalence in soldiers subjected to numerous improvised explosive device attacks. Specifically, severe eye injury was observed in 27.6% of blast survivors, which significantly influences the military service and life experience for soldiers. Currently, some computational models have been established to simulate the impact of the blast on the anterior part of the eyeball. While the blast influence on the posterior part of the eyeball, such as lamina cribrosa and cerebrospinal fluid, still remains unclear. In this study, we characterized the biomechanics of posterior part of the eyeball following blast wave to better understand the mechanism of blast wave transmission by finite element modeling. The result showed that blast loading could induce high strain in lamina cribrosa which exceeded its physiological loading range, the cerebrospinal pressure was also significantly elevated against its normal range. Such biomechanical changes in the posterior part of the eyeball may be attributed to the high prevalence of vision loss following the blast.
Mutual Information Upper Bound of Molecular Communication Based on Cell Metabolism
Massimiliano Pierobon, Zahmeeth Sakkaff, Jennie L. Catlett and Nicole R. Buan
Synthetic biology: tools to engineer cells and their chemical information processing. How to communicate to cells from outside? Molecular communication: study of chemical information exchange. How to realize devices (e.g., for the Internet of Bio-Nano Things)? In this paper, molecular communication concepts are applied to study the potential of cell metabolism, and its regulation, to be utilized to control engineered cells from the external environment. The main contributions are as follows: A communication system abstraction: cell metabolism as binary encoder of external environment chemical composition. A mutual information upper bound: obtained through a well-known and computationally efficient metabolic simulation technique from knowledge of cell DNA code(genome).
Disturbed Wall Mechanics in a cuffed ApoE-/- Mouse Carotid Artery
Caleb C. Berggren, Ryan M. Pedrigi
Plaques are the defining feature of atherosclerosis, which is a leading cause of heart attacks and strokes within the Western world as plaques can rupture and invoke blood clots. Earlier studies have shown altered blood flow-induced shear stress can lead to the development of unstable plaque phenotypes. Our previous study induced disturbed flow regions by positioning a conical cuff on a mouse carotid artery, yielding several types of advanced plaques. Computational fluid dynamics (CFD) simulations were performed and low wall shear stress was suggested to produce unstable plaque types. In this particular study, a finite element analysis is performed to determine the impact of cuff placement alone on unstable plaque development. Cuff placement yields higher circumferential tensile stress in the cuffed region and lower maximum circumferential compressive stress in all regions except the cuff outlet. This lower circumferential tensile stress is hypothesized to similarly produce vulnerable plaque phenotypes.
DISRUPTING ERBB2 TUMOR PROGRESSION VIA JAK1/STAT3 SIGNALING
Barbara Swenson, Patrick Rädler, Aleata Triplett, and Kay-Uwe Wagner
Purpose: The majority of breast cancers are driven by hormones and growth factors, a subset of which overexpress the human epidermal growth factor ErbB2/Her2. While these cancers overexpress ErbB2 they also rely on IL-6 signaling and exhibit high activity of the Signal Transducer and Activator of Transcription 3 (STAT3). A notorious transcription factor, active STAT3 has been tightly linked to aggressive disease, promoting a metastatic phenotype in these breast cancers. While current treatments against ErbB2 are effective, resistance to targeted therapies against this receptor tyrosine kinase and metastasis is a growing problem, and understanding the mechanisms by which inflammatory cytokines contribute to aggressive behavior of breast cancer cells is an important area of research. We have recently demonstrated that Janus kinase 1 (JAK1) is the essential kinase responsible for mediating inflammatory cytokine signaling in the mammary gland. Ablation of JAK1 in the mammary epithelium of mice dramatically prevented the activation of STAT3 downstream of IL-6 class ligands such as Oncostatin M (OSM) and Leukemia inhibitory factor (LIF). While past studies have shown that STAT3 deficient mammary tumors have a reduced propensity to metastasize in an ErbB2-driven breast cancer model, current attempts at directly targeting STAT3 with pharmacological agents have had very limited success. Based on our evidence that JAK1 specifically mediates the effect of inflammatory cytokines to activate STAT3 in the mammary gland, we hypothesize that JAK1 is essential for the persistent activation of STAT3 during the progression of ErbB2-associated mammary cancer. Materials and Methods: MMTV-neu transgenic female mice, where expression of oncogenic ErbB2 is under control of the mouse mammary tumor virus promoter (MMTV-LTR), were crossed into the JAK1 conditional knockout strain that was recently generated by our team (MMTV-Cre Jak1fl/fl). Experimental females carrying the ErbB2 oncogene in addition to the MMTV-Cre transgene in a Jak1 floxed background (MMTV-neu MMTV-Cre Jak1fl/fl) express the Cre recombinase and delete the Jak1 gene prior to or during tumor onset and progression in this model. Littermate control females that lack the MMTV-Cre transgene and therefore express JAK1 were maintained as controls (MMTV-neu MMTV-Cre Jak1fl/fl). Tumors were collected for analysis by immunoblot and immunofluorescent staining. We also derived primary cancer cells from two control tumors to delete the Jak1 gene in fully neoplastic cells using a retroviral expression of Cre recombinase, thereby generating two pairs of isogenic cancer cell lines with and without JAK1 for mechanistic studies and analysis of STAT protein activation. The isogenic cell lines underwent RNA sequencing, followed by differential gene expression analysis and Gene Set Enrichment Analysis, to identify novel deregulated pathways associated with loss of Jak1 controlled STAT activation. Furthermore, the isogenic cell lines were then infected with a lentivirus containing a luciferase reporter and transplanted into the mammary glands of Athymic nude mice and monitored for tumor growth using a caliper and bioluminescent imaging. In a parallel experiment, we used human Her2-positive breast cancer cells and infected with a lentiviral-based, doxycycline (Dox)-inducible shRNA constructs targeting JAK1 and analyzed these cells for deregulated STAT activation. Results: Control and experimental females lacking JAK1 prior to tumor onset have similar tumor latency, suggesting that signaling through JAK1 is not essential for cancer initiation. However, we observed a significant decrease in the engraftment and growth of neoplastic cells when the Jak1 gene was deleted in cancer cells that were then transplanted into wildtype recipient mice. Molecular studies using murine and human cancer cell lines with and without JAK1 revealed that this kinase is essential for the activation of STAT3 as well as STAT1 and STAT6. Hence, other Janus kinases that are expressed in mammary cancer cells (i.e., JAK2 and Tyk2) are not able to compensate for the loss of JAK1. Using RNA-sequencing, we identified several candidate genes that are controlled by JAK1 and that are known to correlate with active STAT3. One of these candidate targets is the protooncogene c-FOS, which was confirmed using immunofluorescent staining on histological sections of mammary cancer specimens that express or lack JAK1. GSEA revealed deregulation of pathways related to focal adhesion and cell adhesion, suggesting Jak1 to be a potential signaling node for metastatic behavior. Conclusion: Our study provides experimental evidence that JAK1 is an essential mediator of inflammatory cytokine signaling in breast cancer cells, and this tyrosine kinase plays a critical role in mammary cancer progression. In contrast to the current paradigm, JAK1 has nonredundant functions for the activation of STAT1, 3, and 6 that are persistently tyrosine phosphorylated in mammary cancer cells. Lack of JAK1 and oncogenic STAT3 inhibits the expression of other tumor susceptibility gens such as c-FOS. Our collective findings suggest that JAK1 is a rational target to prevent breast cancer progression.
Impact of Aspergillus nidulans Signaling Mutants on Growth ans Secretion on Non-preferred Carbon Sources
Genome-wide detection of alternative splicing in Arabidopsis thaliana TGH mutant using ribo-removal RNA-seq
Alternative splicing (AS) dramatically increases the transcriptome and proteome diversity by producing multiple splice variants from different combinations of exons. The accumulating genome sequences and highthroughput data have shed light on the extent and importance of alternative splicing in functional regulation in plants. miRNA pathway regulation in plants such as Arabidopsis may arise from alternative splicing and its regulation. Here we detect the genome-wide novel AS events from ribo-removal RNAseq analysis in Arabidopsis between wild type and TGH mutant and characterize their potential effects on their function and miRNA regulation.
Origin and Tuning of the Magnetic Anisotropy in CeCo5-based Alloys
Ivan Zhuravlev, M. Daene, Kirill Belashchenko
World demand in permanent magnets constantly grows. Thus search and development of new magnetic materials is very crucial. CeCo5-based alloys are good candidates for permanent magnets application. Pure CeCo5 has decent magnetic properties. Magnetocrystalline anisotropy (MCA) in CeCo5 comparable to the best in field Nd-based permanent magnets while Curie temperature in CeCo5 is much higher. Investigation of CeCo5-based alloys is interesting from both point of view theoretical and economical. In the series of RCo5 alloys CeCo5 has several anomalies. From economical point of view CeCo5 can be used for applications where SmCo5 or Nd-based magnets are too expensive but cheeper magnets like AlNiCo do not provide appropriate magnetic parameters.
This work was mainly focused on theoretical investigation of magnetocrystalline anisotropy and its origin in CeCo5- based alloys with Fe, Ni and Cu doping. For the calculation of magnetic properties the Green function-based formulation of the tight-binding linear muffin-tin orbital (GF-LMTO) method in the atomic sphere approximation (ASA) was used. Substitutional disorder was treated using the coherent potential approximation. Our results show enhancement of anisotropy up 40% compare to pure CeCo5. Maximum in MCA reached at 12% for Fe, 11% for Ni and 7% for Cu. Variation of MCA was associated with selective suppression of spin-orbit «hot spots" in GMK plane.